Figure REF  illustrates several of the spatial components extracted by our pipeline (again, see the corresponding video for a more detailed illustration of the demixing performance); these components visually appear to be dendritic segments and match well with the signals visible in the data movie. Notably, no parameter tuning was necessary to obtain good demixing performance on both the VI and CI datasets, despite the many differences between these data types. Additionally, as a baseline comparison we applied a simple sparse NMF approach with random initialization (similar to the method described in [1]}) to both the denoised and raw data (\(\hat{\mathbf {Y}}\)  and \(\mathbf {Y}\) , respectively). As shown in the right columns of Figure REF , this baseline approach extracted components that were much more mixed and noisy than the components extracted by our proposed demixing pipeline; we also found that the baseline approach was more prone to missing weaker, dimmer components than was the proposed pipeline (data not shown).
